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Webinars

Date of Webinar: July 22, 2021

Watch our keynote presentation recorded July 22, 2021, at the virtual MIT CDOIQ Symposium and get key insights to help you build trusted data strategies for innovation that benefits everyone.

Webinars

Date of Webinar: July 16, 2021

Your organization’s data is a powerful and valuable resource. But using it in a way that fails to protect individual privacy under regulations such as GDPR, HIPAA, and CCPA can lead to hefty fines, lost business, and liability. To lead in a competitive marketplace, and gain a greater advantage, you need solutions that allow you to safely use and share sensitive data, without risking privacy.

Webinars

Date of Webinar: June 24, 2021

Your structured clinical trial data is an increasingly valuable resource that benefits new analytics, research, and innovation. With the pressure being put on pharma to collaborate and share trial data, your organization wants to step up and contribute, but patient-level data contains large amounts of sensitive information, regulators and the public need to see that privacy is protected, and protecting privacy often means losing data utility. Fortunately, Privacy Analytics can help you overcome each of these challenges.

Webinars

Date of Webinar: June 10, 2021

If you want to safely leverage the increasing value of your sensitive data by implementing data anonymization at scale, you’re likely facing one (or all) of these critical challenges: managing high volumes, varied requests, and short turnaround times; finding people with the skills needed to achieve high data utility; and adopting a defensible, standardized approach to anonymization. Fortunately, software can help you overcome each of these challenges.

Webinars

Date of Webinar: April 8, 2021

In this brief webinar, Niamh McGuinness, Senior CTT Analyst, will be showcasing Privacy Analytics’ new Kick-Start service offer for clinical trial data anonymization. It enables you to quickly apply our proven statistical method to your structured individual patient data (S-IPD), without having to commit for the long term.

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